Income inequality

Data on income inequality in various countries comes from the OECD Income Distribution Database (IDD). The IDD offers data on levels and trends in income inequality and poverty, and is updated on a rolling basis. The latest update at the time of writing was in June 2025. You can download a summary table with key indicators such as Gini coefficients, income share, quintile share ratios and poverty rates for selected years from here. An interactive charting tool of income inequality indicators is also available here. Detailed definitions and technical descriptions of their operationalisation is provided here. You can read a summary of the database and core inequality indicators in the drop-down box below.

OECD data sources and definitions

Equivalised household disposable income

The OECD Income Distribution database (IDD) benchmarks and monitors countries’ performance in the field of income inequality and poverty. It contains a number of standardised indicators based on the central concept of “equivalised household disposable income”, i.e. the total income received by the households less the current taxes and transfers they pay, adjusted for household size with an equivalence scale. While household income is only one of the factors shaping people’s economic well-being, it is also the one for which comparable data for all OECD countries are most common. Income distribution has a long-standing tradition among household-level statistics, with regular data collections going back to the 1980s (and sometimes earlier) in many OECD countries.

Achieving comparability in this field is a challenge, as national practices differ widely in terms of concepts, measures, and statistical sources. In order to maximise international comparability as well as inter-temporal consistency of data, the IDD data collection and compilation process is based on a common set of statistical conventions (e.g. on income concepts and components). The information obtained by the OECD through a network of national data providers, via a standardized questionnaire, is based on national sources that are deemed to be most representative for each country. The original data sources for each country and year are listed here.

Quintile share ratio (disposable income)

The quintile share ratio - or S80/S20 ratio - is the share of all income received by the top quintile (i.e. 20%) divided by the share of the bottom quintile (i.e. 20%). It is a relatively simple measure to calculate once we have accurate data on the income distribution in a country. For example, in the IDD key indicators summary table we find that in Australia in the latest available year earners in the bottom 20% of the income distribution held 7.2% of the total income distributed in that country, while those in the top 20% of the distribution received 40% of that total. If we divide the two (\(frac{40}{7.2}\)) we obtain r round(40/7.2, 1), which is the value given for the S80/S20 income share ratio indicator for Australia in the latest available year.

Gini (disposable income)

The Gini coefficient is a measure that compares cumulative proportions of the population against the cumulative proportions of income they receive, condensing the entire income distribution for a country into a single number between 0 and 1: the higher the number, the greater the degree of income inequality. Mathematically, there are a few different equations that economists commonly use for calculating it, with those based on the so-called Lorenz curve being the most popular. You can watch a very short video explanation of the Gini coefficient here and there is a nice and simple online Gini Coefficient Calculator available here. For example, look at the two sets of numbers below, which we can imagine to represent the incomes of ten individuals each making up the population of a different country (and assume some standardised hypothetical currency unit that equalises purchasing power differences in the two countries)¹:

• Country A: 8000 10000 9000 10000 10000 8000 7000 8000 390000 540000
• Country B: 80000 100000 90000 120000 140000 70000 70000 90000 120000 120000

Eyeballing the numbers, which “country” do you think is the more equal one, and which is the more unequal one? Which one will have the higher Gini coefficient? You can copy/paste each set of numbers into the online calculator to get the precise coefficient. But the true reason why we may care about these artificial metrics is that it allows us to ask some further questions, such as: which individual would you most like to be from among the twenty income holders listed?; which country would you rather like to live in?; if you were to be randomly assigned at birth to a country in a world consisting of several countries such as these two, would you be more comfortable if that world consisted predominantly of countries of type “A” or “B”? Philosophers have been asking these questions - sometimes very explicitly - for a long time, economists have been working on designing more detailed and accurate measurements and modelling techniques, and sociologists are always interested in understanding how these questions shape the actual lives of people.

We can view and download any relevant data through the OECD website’s Data Explorer, and we selected two measures of income inequality - the Gini index and the S80/S20 ratio - for all years between 2000-2023 as measured based on various definitions:

This is the dataset, downloaded to a .csv file named oecd-inequality.csv, which we will use to obtain the income inequality macro data needed to replicate the analysis presented in Wilkinson and Pickett (2010) and Pickett et al. (2024) (see also Pickett (2024)).

References

Pickett K (2024) The Spirit Level at 15 – Technical Appendix. July. London: The Equality Trust.

Pickett K, Gauhar A and Wilkinson R (2024) The Spirit Level at 15: The Enduring Impact of Inequality. Monograph, July. London: The Equality Trust.

Wilkinson RG and Pickett K (2010) The Spirit Level: Why Greater Equality Makes Societies Stronger. New York: Bloomsbury Press.

Footnotes

1. Many internationally comparative economic indicators rely on such standardised units as Purchasing Power Parity (PPP) rates, international dollars, Purchasing Power Standards (PPS) or the ‘Big Mac Index’